AI-Powered Plant Disease Classification: Innovating for Sustainable Agriculture
DOI:
https://doi.org/10.36676/jrps.2023-v14i5-09Keywords:
research, overfitting, management of plantAbstract
This research explores the development and application of artificial intelligence (AI) models in Python for plant disease classification. Using a large training dataset with over 50,000 images representing various plant conditions, the study highlights the effectiveness of AI and computer vision by achieving approximately 86% accuracy in correctly identifying the plant disease. It demonstrates a balanced approach to maintaining accuracy while avoiding overfitting, underscoring AI's potential in agriculture.
References
Abdallah Ali Dev. "PlantVillage Dataset." Kaggle, n.d., https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset.
He, Kaiming, et al. "Deep Residual Learning for Image Recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. DOI: https://doi.org/10.1109/CVPR.2016.90
Kingma, Diederik P., and Jimmy Ba. "Adam: A Method for Stochastic Optimization." arXiv preprint arXiv:1412.6980, 2014.
Bottou, Léon. "Large-Scale Machine Learning with Stochastic Gradient Descent." Proceedings of COMPSTAT'2010, Physica-Verlag HD, 2010, pp. 177-186. DOI: https://doi.org/10.1007/978-3-7908-2604-3_16
Mohanty, Sharada P., David P. Hughes, and Marcel Salathé. "Using Deep Learning for Image-Based Plant Disease Detection." Frontiers in Plant Science, vol. 7, 2016, p. 1419, Frontiers. DOI: https://doi.org/10.3389/fpls.2016.01419
Plant Methods Editors. "Plant Diseases and Pests Detection Based on Deep Learning: A Review." Plant Methods, vol. 17, no. 22, 2021. DOI: https://doi.org/10.1186/s13007-021-00722-9
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.